{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     crim    zn  indus  chas    nox     rm   age     dis  rad  \\\n",
       "0           1  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1   \n",
       "1           2  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2   \n",
       "2           3  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2   \n",
       "3           4  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3   \n",
       "4           5  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3   \n",
       "\n",
       "   tax  ptratio   black  lstat  medv  \n",
       "0  296     15.3  396.90   4.98  24.0  \n",
       "1  242     17.8  396.90   9.14  21.6  \n",
       "2  242     17.8  392.83   4.03  34.7  \n",
       "3  222     18.7  394.63   2.94  33.4  \n",
       "4  222     18.7  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"dataset/boston.csv\", header=0)\n",
    "# data.drop([\"class\"], axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegression:\n",
    "    '''使用梯度下降回归算法'''\n",
    "    def __init__(self, learning_rate, times):\n",
    "        '''初始化\n",
    "        parameters\n",
    "        -----\n",
    "        learn_rate: 学习率。梯度下降步长 \n",
    "        times: 迭代次数\n",
    "        '''\n",
    "        self.learning_rate = learning_rate\n",
    "        self.times = times\n",
    "    def fit(self, X, y):\n",
    "        '''对样本进行预测\n",
    "        Parameters:\n",
    "        X: 特征矩阵，可以是List也可以是Ndarray，形状为： [样本数量,特征数量]\n",
    "        y: 标签数组\n",
    "        '''\n",
    "        X = np.asarray(X)\n",
    "        y = np.asarray(y)\n",
    "        # 创建权重，初始值为0(也可以试试其他值)，* 长度比X特征数多1，多出的就是w0\n",
    "        self.w_ = np.zeros(1 + X.shape[1]) # X.shape[1] 为特征数\n",
    "        # 创建损失值列表，用来每次迭代的损失值。\n",
    "        # 损失值计算公式：(1/2 )*( 预测值 - 真实值)^2\n",
    "        self.loss_ = []\n",
    "        # 开始迭代\n",
    "        for x in range(self.times):\n",
    "            # 为什么使用 self.w_[1:] ？ 因为X特征个数比w少1个，所以这里w0先去掉，点乘后再加上\n",
    "            # 矩阵可以使用*运算，但是这里是ndarray数组只能用点乘，1表示x0\n",
    "            y_hat = np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "            # 计算误差\n",
    "            error = y - y_hat\n",
    "            # 将损失值加入损失列表\n",
    "            self.loss_.append(np.sum(error**2)/2)\n",
    "            # 根据差距调整权重w_\n",
    "            # 调整为 w(j)=w(j)+learn_rate*sum((y - y_hat)*x(j))\n",
    "            # 更新w0，x看成1直接更新\n",
    "            self.w_[0] += self.learning_rate * np.sum(error * 1)\n",
    "            # ****************************************************\n",
    "            # ************  重点：这里一定要搞明白 ***************\n",
    "            # ****************************************************\n",
    "            # 使用x1到xn的某一列向量(即某一特征)的转置来更新w，也就是将w向x进拟合\n",
    "            self.w_[1:] += self.learning_rate * np.dot(X.T, error)\n",
    "    def predict(self, X):\n",
    "        '''根据参数进行预测\n",
    "        Paramaters\n",
    "        -----\n",
    "        X： [样本数量, 特征数量]\n",
    "        -----\n",
    "        Returns: 预测结果\n",
    "        '''\n",
    "        X = np.asarray(X)\n",
    "        result = np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StandardScaler:\n",
    "    '''对数据集进行标准化处理'''\n",
    "    def fit(self, X):\n",
    "        '''根据传递的样本，计算每个特征列的均值和标准差\n",
    "        Parameters\n",
    "        -----\n",
    "        X: 训练数据。用来计算均值和标准差\n",
    "        '''\n",
    "        X = np.asarray(X)\n",
    "        self.std_ = np.std(X, axis=0) # 按列计算标准差\n",
    "        self.mean_ = np.mean(X, axis=0) # 按列计算#值\n",
    "    def transform(self, X):\n",
    "        '''对给定数据进行标准化处理\n",
    "        * 将X每一列都变成标准正太分布的形式，即满足均值为0，标准差为1\n",
    "        * 标准化也叫标准差标准化，经过处理的数据符合标准正态分布。\n",
    "        \n",
    "        Parameters\n",
    "        -----\n",
    "        X: 类数组类型。待转换的数据\n",
    "        '''\n",
    "        return (X - self.mean_) / self.std_\n",
    "    def fit_transform(self, X):\n",
    "        ''' fit后transform\n",
    "        '''\n",
    "        self.fit(X)\n",
    "        return self.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1804176210461773e+210"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([-2.68833075e+099, -7.57608494e+101, -1.29174503e+100,\n",
       "       -2.67102000e+100, -3.24034515e+100, -1.81386180e+098,\n",
       "       -1.52555272e+099, -1.67597187e+100, -1.90402088e+101,\n",
       "       -9.69839266e+099, -3.06079801e+100, -1.19382162e+102,\n",
       "       -5.02206239e+100, -9.48116678e+101, -3.57226707e+100])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[116831.44,\n",
       " 2083552221174028.0,\n",
       " 4.883479637283036e+25,\n",
       " 1.14536821770387e+36,\n",
       " 2.686348040018552e+46,\n",
       " 6.300564127408661e+56,\n",
       " 1.477735116047291e+67,\n",
       " 3.465881830645266e+77,\n",
       " 8.128883677155966e+87,\n",
       " 1.9065494170189406e+98,\n",
       " 4.4716234404365476e+108,\n",
       " 1.0487751334726054e+119,\n",
       " 2.4597985390359684e+129,\n",
       " 5.769214638613026e+139,\n",
       " 1.353112339006075e+150,\n",
       " 3.1735914100271017e+160,\n",
       " 7.443345350908508e+170,\n",
       " 1.7457631703262697e+181,\n",
       " 4.09451517185836e+191,\n",
       " 9.603281119422997e+201]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LinearRegression(learning_rate=0.001, times=20)\n",
    "t = data.sample(len(data), random_state=0)\n",
    "train_X = t.iloc[:400, :-1]\n",
    "train_y = t.iloc[:400, -1]\n",
    "test_X = t.iloc[400:, :-1]\n",
    "test_y = t.iloc[400:, -1]\n",
    "lr.fit(train_X, train_y)\n",
    "result = lr.predict(test_X)\n",
    "# display(result)\n",
    "display(np.mean((result - test_y)**2))\n",
    "display(lr.w_)\n",
    "display(lr.loss_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14911890500740144"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 1.47715173e-16, -2.67726470e-03, -7.77999440e-02,  3.22218582e-02,\n",
       "       -4.15123849e-02,  7.21847299e-02, -1.22496354e-01,  3.18411097e-01,\n",
       "       -8.44203177e-03, -2.09306424e-01,  1.02744566e-01, -5.29297011e-02,\n",
       "       -1.81988334e-01,  9.71339528e-02, -3.93923586e-01])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[200.0,\n",
       " 109.56340745551384,\n",
       " 89.24537638439179,\n",
       " 79.69665810311717,\n",
       " 74.15994999919499,\n",
       " 70.76479029645887,\n",
       " 68.5986097420355,\n",
       " 67.15477786507567,\n",
       " 66.1444617646852,\n",
       " 65.4007703140392,\n",
       " 64.82595473196506,\n",
       " 64.36185638542904,\n",
       " 63.973228857262946,\n",
       " 63.638247945534275,\n",
       " 63.343061508525125,\n",
       " 63.07862649070146,\n",
       " 62.83885168788381,\n",
       " 62.619493019046615,\n",
       " 62.41748706751545,\n",
       " 62.23054285212329]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为了避免每个特征数量级的不同对梯度下降造成的影响。我们对每个特征进行标准化\n",
    "lr = LinearRegression(learning_rate=0.0005, times=20)\n",
    "t = data.sample(len(data), random_state=0)\n",
    "train_X = t.iloc[:400, :-1]\n",
    "train_y = t.iloc[:400, -1]\n",
    "test_X = t.iloc[400:, :-1]\n",
    "test_y = t.iloc[400:, -1]\n",
    "\n",
    "# 标准化\n",
    "ss = StandardScaler()\n",
    "train_X = ss.fit_transform(train_X)\n",
    "test_X = ss.fit_transform(test_X)\n",
    "ss2 = StandardScaler()\n",
    "train_y = ss2.fit_transform(train_y)\n",
    "test_y = ss2.fit_transform(test_y)\n",
    "\n",
    "# 训练 预测\n",
    "lr.fit(train_X, train_y)\n",
    "result = lr.predict(test_X)\n",
    "display(np.mean((result - test_y)**2))\n",
    "display(lr.w_)\n",
    "display(lr.loss_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rcParams[\"font.family\"] = \"SimHei\"\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(result, \"ro-\", label=\"预测值\")\n",
    "plt.plot(test_y.values, \"go-\", label=\"预测值\") # pandas读取时serise类型，我们需要转为ndarray\n",
    "plt.title(\"线性回归预测-梯度下降\")\n",
    "plt.xlabel(\"测试机样本序号\")\n",
    "plt.ylabel(\"预测房价\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ad09de4d88>]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制累计误差值\n",
    "plt.plot(range(1,lr.times+1),lr.loss_, \"o-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.002805351917207"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 因为放假更新涉及多个维度，不方便可视化\n",
    "# 为了可视化，我们只选择其中一个维度（RM），并画出直线，进行拟合\n",
    "lr = LinearRegression(learning_rate=0.0005, times=20)\n",
    "t = data.sample(len(data), random_state=0)\n",
    "train_X = t.iloc[:400, 6:7]\n",
    "train_y = t.iloc[:400, -1]\n",
    "test_X = t.iloc[400:, 5:6]\n",
    "test_y = t.iloc[400:, -1]\n",
    "# 标准化\n",
    "ss = StandardScaler()\n",
    "train_X = ss.fit_transform(train_X)\n",
    "test_X = ss.fit_transform(test_X)\n",
    "ss2 = StandardScaler()\n",
    "train_y = ss2.fit_transform(train_y)\n",
    "test_y = ss2.fit_transform(test_y)\n",
    "lr.fit(train_X, train_y)\n",
    "result = lr.predict(test_X)\n",
    "# display(result)\n",
    "display(np.mean((result - test_y)**2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.05755421e-16,  6.54993957e-01])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ad09a17248>]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示rm对对于价格的影响\n",
    "plt.scatter(train_X[\"rm\"], train_y)\n",
    "# 展示权重\n",
    "display(lr.w_)\n",
    "# 构建方程 y = w0 + x*w1 = -3.05755421e-16 + x *  6.54993957e-01\n",
    "x = np.arange(-5,5,0.1)\n",
    "y = -3.05755421e-16 + x *  6.54993957e-01\n",
    "plt.plot(x, y, \"g\") # 绿色直线显示我们的拟合直线\n",
    "# *********  x.reshape(-1,1) 把一维转为二位 ****************\n",
    "plt.plot(x, lr.predict(x.reshape(-1,1)), 'r')\n",
    "\n",
    "# 可以看到我们预测的和你和的完全重合了"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
